SoroushVahidi/scidocs huggingface dataset:
Deep Domain Adaptation in Action Space
(2018)

In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.
Parallel Computer Architecture: A Hardware/Software Approach
(1999)

enter the circuit several times, possibly via different input processors. More elaborate combinatorial considerations than for standard VLSI circuits are needed to show how communication complexity can be used to get lower bounds on multilective VLSI computations. Near the end of the book, Hromkovic presents relations between communication complexity and some complexity measures of sequential computations. The core section introduces a uniform model of one-way communication protocols and shows that the corresponding uniform one-way communication complexity is strongly related to the size of deterministic finite automata. Then he relates communication complexity to the time and space complexity of Turing machines. Finally, he shows how communication complexity can be used to obtain lower bounds on the size and the depth of decisions nees and branching programs.
Sub-Nyquist Radar via Doppler Focusing
(2014)

We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here, we describe a sub-Nyquist sampling and recovery approach called Doppler focusing, which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size, which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement, which scales linearly with P, obtaining good detection performance even at SNR as low as - 25 dB. The recovery is based on the Xampling framework, which allows reduction of the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype.
The diffusion of misinformation on social media: Temporal pattern, message, and source
(2018)

This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temp oral attern, content mutation, and sources of misinformation. We traced the lifecycle of 17 po pular political rumors that circulated on Twitter over 13 months during the 2012 U.S. preside ntial election. Using text analysis based on time series, we found that while false rumors (misi nformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension aro und the target dissolves. We observed that rumors resurface by partisan news websites that rep ckage the old rumor into news and, gain visibility by influential Twitter users who introdu ce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mecha nism by which these messages evolve over time.
Autonomous railway crack detector robot for bangladesh: SCANOBOT
(2017)

An automatic railway track crack detector system for Bangladesh Railway has been proposed here which aims in building a robot that can detect and analyze any kind of crack on the railway line and send the coordinates of that faulty line to the concerned authority. This robot includes two ultrasonic sensors, GPS, GSM modules, and Arduino Mega based crack detection assembly which is cost effective and robust to facilitate better safety standards in railways. As soon as the robot passed through a crack that might cause the derailment of a train, the ultrasonic sensors sense that and generate a signal. Then this signal is fed into the Arduino Mega. At that point, with the assistance of GSM and GPS modules, an alert SMS consist of the geographic coordinate of that damaged track is sent to the nearby railway authority who can easily take necessary steps to resolve the problem before any major accident occurs. This will save several trains in Bangladesh from an unwanted discontinuity from the rail track.
End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection
(2018)

Object detection methods usually represent objects through rectangular bounding boxes from which they extract features, regardless of their actual shapes. In this paper, we apply deformations to regions in order to learn representations better fitted to objects. We introduce DP-FCN, a deep model implementing this idea by learning to align parts to discriminative elements of objects in a latent way, i.e. without part annotation. This approach has two main assets: it builds invariance to local transformations, thus improving recognition, and brings geometric information to describe objects more finely, leading to a more accurate localization. We further develop both features in a new model named DP-FCN2.0 by explicitly learning interactions between parts. Alignment is done with an in-network joint optimization of all parts based on a CRF with custom potentials, and deformations are influencing localization through a bilinear product. We validate our models on PASCAL VOC and MS COCO datasets and show significant gains. DP-FCN2.0 achieves state-of-the-art results of 83.3 and 81.2% on VOC 2007 and 2012 with VOC data only.
Suicidal strangulation by double ligature: a case report.
(2014)

This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.
An Evaluation of Distributed Concurrency Control
(2017)

Increasing transaction volumes have led to a resurgence of interest in distributed transaction processing. In particular, partitioning data across several servers can improve throughput by allowing servers to process transactions in parallel. But executing transactions across servers limits the scalability and performance of these systems. In this paper, we quantify the effects of distribution on concurrency control protocols in a distributed environment. We evaluate six classic and modern protocols in an in-memory distributed database evaluation framework called Deneva, providing an apples-to-apples comparison between each. Our results expose severe limitations of distributed transaction processing engines. Moreover, in our analysis, we identify several protocol-specific scalability bottlenecks. We conclude that to achieve truly scalable operation, distributed concurrency control solutions must seek a tighter coupling with either novel network hardware (in the local area) or applications (via data modeling and semantically-aware execution), or both.
Hierarchical Self-Organization in Genetic programming
(1994)

This paper presents an approach to automatic discovery of functions in Genetic Programming. The approach is based on discovery of useful building blocks by analyzing the evolution trace, generalizing blocks to define new functions, and finally adapting the problem representation onthe-fly. Adaptating the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Measures of complexity of solution trees are defined for an adaptive representation framework. The minimum description length principle is applied to justify the feasibilityof approaches based on a hierarchy of discovered functions and to suggest alternative ways of defining a problem’s fitness function. Preliminary empirical results are presented.
Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers
(2014)

An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.
Deep Sentiment Representation Based on CNN and LSTM
(2017)

Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).
What Do Customers Crave in Mobile 5G?: A survey spotlights four standout factors.
(2017)

Increasing customer demand coupled with limitations in current fourth-generation (4G) wireless mobile networks and advances toward the next-generation wireless mobile network have all together created a need for us to look at what customers today expect from the upcoming fifth-generation (5G) network. This study was conducted among existing 4G users in the United States, the United Kingdom, Singapore, and Australia. The research was completed in two parts: qualitative and quantitative. The quantitative part was accomplished by performing data collection and selection through surveys and data evaluation by structural equation modeling (SEM) using SMARTPLS.
Multi-target tracking scheme using a track management table for automotive radar systems
(2016)

In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.
Technochange management: using IT to drive organizational change
(2004)

Using IT in ways that can trigger major organizational changes creates high-risk, potentially high-reward, situations that I call technochange (for technology-driven organizational change). Technochange differs from typical IT projects and from typical organizational change programs and therefore requires a different approach. One major risk in technochange—that people will not use information technology and related work practices—is not thoroughly addressed by the discipline of IT project management, which focuses on project cost, project schedule, and solution functionality. Organizational change management approaches are also generally not effective on their own, because they take as a given the IT ‘‘solutions’’ developed by a technical team. Consequently, the potential for the IT ‘‘solution’’ to be misaligned with important organizational characteristics, such as culture or incentives, is great. Merely combining IT project management and organizational change management approaches does not produce the best results, for two reasons. First, the additive approach does not effectively address the many failure-threatening problems that can arise over the lengthy sequential process of the typical technochange lifecycle. Second, the additive approach is not structured to produce the characteristics of a good technochange solution: a complete intervention consisting of IT and complementary organizational changes, an implementable solution with minimal misfits with the existing organization, and an organization primed to appropriate the potential bene¢ts of the technochange solution. With hard work and care, the combined IT project management plus organizational change approach can be made to work. However, an iterative, incremental approach to implementing technochange can be a better strategy in many situations. The essential characteristic of the technochange prototyping approach is that each phase involves both new IT functionality and related organizational changes, such as redesigned business processes, new performance metrics, and training. Journal of Information Technology (2004) 19, 4–20. doi:10.1057/palgrave.jit.2000002
An efficient FPGA-Based architecture for convolutional neural networks
(2017)

The goal of this paper is to implement an efficient FPGA-based hardware architectures for the design of fast artificial vision systems. The proposed architecture is capable of performing classification operations of a Convolutional Neural Network (CNN) in realtime. To show the effectiveness of the architecture, some design examples such as hand posture recognition, character recognition, and face recognition are provided. Experimental results show that the proposed architecture is well suited for embedded artificial computer vision systems requiring high portability, high computational speed, and accurate classification.
A BigData approach for classification and prediction of student result using MapReduce
(2015)

In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as additional data becomes available. The predictive models will help the instructor to understand how well or how poorly the students in his/her class will perform, and hence the instructor can choose proper pedagogical and instructional interventions to enhance student learning outcomes. The implementation is done in Hadoop framework with MapReduce and Revolutionary R Enterprise RRE.
“ Proof-of-Work ” Proves Not to Work version 0 . 1
(2004)

A frequently proposed method of reducing unsolicited bulk email (“spam”) is for senders to pay for each email they send. Proof-ofwork schemes avoid charging real money by requiring senders to demonstrate that they have expended processing time in solving a cryptographic puzzle. We attempt to determine how difficult that puzzle should be so as to be effective in preventing spam. We analyse this both from an economic perspective, “how can we stop it being cost-effective to send spam”, and from a security perspective, “spammers can access insecure end-user machines and will steal processing cycles to solve puzzles”. Both analyses lead to similar values of puzzle difficulty. Unfortunately, realworld data from a large ISP shows that these difficulty levels would mean that significant numbers of senders of legitimate email would be unable to continue their current levels of activity. We conclude that proof-of-work will not be a solution to the problem of spam.
Strong Injection Locking in Low- $Q$ LC Oscillators: Modeling and Application in a Forwarded-Clock I/O Receiver
(2009)

A general model for injection-locked LC oscillators (LC-ILOs) is presented that is valid for any tank quality factor and injection strength. Important properties of an ILO such as lock-range, phase shift, bandwidth and response to input jitter are described. An LC-ILO together with a half-rate data sampler is implemented as a forwarded-clock I/O receiver in 45-nm CMOS. A strongly-injected low-Q LC oscillator enables clock deskew across 1UI and rejects high-frequency clock jitter. The complete 27 Gb/s ILO-based data receiver has an overall power efficiency of 1.6 mW/Gb/s.
Acoustic Modeling for Google Home
(2017)

This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and Grid-LSTMs to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of 8-28% relative compared to the current production system.
Path Reducing Watershed for the GPU
(2018)

The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.
Learning-based approach for online lane change intention prediction
(2013)

Predicting driver behavior is a key component for Advanced Driver Assistance Systems (ADAS). In this paper, a novel approach based on Support Vector Machine and Bayesian filtering is proposed for online lane change intention prediction. The approach uses the multiclass probabilistic outputs of the Support Vector Machine as an input to the Bayesian filter, and the output of the Bayesian filter is used for the final prediction of lane changes. A lane tracker integrated in a passenger vehicle is used for real-world data collection for the purpose of training and testing. Data from different drivers on different highways were used to evaluate the robustness of the approach. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 1.3 seconds in advance, with a maximum prediction horizon of 3.29 seconds.
On the Role of Seed Lexicons in Learning Bilingual Word Embeddings
(2016)

A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SBWES induction is to learn a mapping function between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used in the learning process. In this work, we analyze the importance and properties of seed lexicons for the SBWES induction across different dimensions (i.e., lexicon source, lexicon size, translation method, translation pair reliability). On the basis of our analysis, we propose a simple but effective hybrid bilingual word embedding (BWE) model. This model (HYBWE) learns the mapping between two monolingual embedding spaces using only highly reliable symmetric translation pairs from a seed document-level embedding space. We perform bilingual lexicon learning (BLL) with 3 language pairs and show that by carefully selecting reliable translation pairs our new HYBWE model outperforms benchmarking BWE learning models, all of which use more expensive bilingual signals. Effectively, we demonstrate that a SBWES may be induced by leveraging only a very weak bilingual signal (document alignments) along with monolingual data.